Metrx MCP Server

Your AI agents are wasting money. Metrx finds out how much, and fixes it.

The official MCP server for Metrx — the AI Agent Cost Intelligence Platform. Give any MCP-compatible agent (Claude, GPT, Gemini, Cursor, Windsurf) the ability to track its own costs, detect waste, optimize model selection, and prove ROI.

Why Metrx?

Problem	What Metrx Does
No visibility into agent spend	Real-time cost dashboards per agent, model, and provider
Overpaying for LLM calls	Provider arbitrage finds cheaper models for the same task
Runaway costs	Budget enforcement with auto-pause when limits are hit
Wasted tokens	Cost leak scanner detects retry storms, context bloat, model mismatch
Can't prove AI ROI	Revenue attribution links agent actions to business outcomes

Quick Start

Try it now — no signup required

npx @metrxbot/mcp-server --demo

This starts the server with sample data so you can explore all 23 tools instantly.

Connect your real data

Option A — Interactive login (recommended):

npx @metrxbot/mcp-server --auth

Opens your browser to get an API key, validates it, and saves it to ~/.metrxrc so you never need to set env vars.

Option B — Environment variable:

METRX_API_KEY=sk_live_your_key_here npx @metrxbot/mcp-server --test

Get your free API key at app.metrxbot.com/sign-up.

Add to your MCP client (Claude Desktop, Cursor, Windsurf)

If you used --auth, no env block is needed — the key is read from ~/.metrxrc automatically:

{
  "mcpServers": {
    "metrx": {
      "command": "npx",
      "args": ["@metrxbot/mcp-server"]
    }
  }
}

Or pass the key explicitly via environment:

{
  "mcpServers": {
    "metrx": {
      "command": "npx",
      "args": ["@metrxbot/mcp-server"],
      "env": {
        "METRX_API_KEY": "sk_live_your_key_here"
      }
    }
  }
}

Remote HTTP endpoint

For remote agents (no local install needed):

POST https://metrxbot.com/api/mcp
Authorization: Bearer sk_live_your_key_here
Content-Type: application/json

From npm

npm install @metrxbot/mcp-server

23 Tools Across 10 Domains

Dashboard (3 tools)

Tool	Description
`metrx_get_cost_summary`	Comprehensive cost summary — total spend, call counts, error rates, and optimization opportunities
`metrx_list_agents`	List all agents with status, category, cost metrics, and health indicators
`metrx_get_agent_detail`	Detailed agent info including model, framework, cost breakdown, and performance history

Optimization (4 tools)

Tool	Description
`metrx_get_optimization_recommendations`	AI-powered cost optimization recommendations per agent or fleet-wide
`metrx_apply_optimization`	One-click apply an optimization recommendation to an agent
`metrx_route_model`	Model routing recommendation for a specific task based on complexity
`metrx_compare_models`	Compare LLM model pricing and capabilities across providers

Budgets (3 tools)

Tool	Description
`metrx_get_budget_status`	Current status of all budget configurations with spend vs. limits
`metrx_set_budget`	Create or update a budget with hard, soft, or monitor enforcement
`metrx_update_budget_mode`	Change enforcement mode of an existing budget or pause/resume it

Alerts (3 tools)

Tool	Description
`metrx_get_alerts`	Active alerts and notifications for your agent fleet
`metrx_acknowledge_alert`	Mark one or more alerts as read/acknowledged
`metrx_get_failure_predictions`	Predictive failure analysis — identify agents likely to fail before it happens

Experiments (3 tools)

Tool	Description
`metrx_create_model_experiment`	Start an A/B test comparing two LLM models with traffic splitting
`metrx_get_experiment_results`	Statistical significance, cost delta, and recommended action
`metrx_stop_experiment`	Stop a running model routing experiment and lock in the winner

Cost Leak Detector (1 tool)

Tool	Description
`metrx_run_cost_leak_scan`	Comprehensive 7-check cost leak audit across your entire agent fleet

Attribution (3 tools)

Tool	Description
`metrx_attribute_task`	Link agent actions to business outcomes for ROI tracking
`metrx_get_task_roi`	Calculate return on investment for an agent — costs vs. attributed outcomes
`metrx_get_attribution_report`	Multi-source attribution report with confidence scores and top contributors

Alert Configuration (1 tool)

Tool	Description
`metrx_configure_alert_threshold`	Set cost or operational alert thresholds with email, webhook, or auto-pause

ROI Audit (1 tool)

Tool	Description
`metrx_generate_roi_audit`	Board-ready ROI audit report for your AI agent fleet

Upgrade Justification (1 tool)

Tool	Description
`metrx_get_upgrade_justification`	ROI report for tier upgrades based on current usage patterns

Prompts

Pre-built prompt templates for common workflows:

Prompt	Description
`analyze-costs`	Comprehensive cost overview — spend breakdown, top agents, optimization opportunities
`find-savings`	Discover optimization opportunities — model downgrades, caching, routing
`cost-leak-scan`	Scan for waste patterns — retry storms, oversized contexts, model mismatch

Examples

"How much am I spending?"

User: What was my AI cost this week?
→ metrx_get_cost_summary(period_days=7)

Total Spend: $234.56 | Calls: 2,450 | Error Rate: 0.2%
├── customer-support: $156.23 (1,800 calls)
└── code-generator: $78.33 (650 calls)

💡 Switch customer-support from GPT-4 to Claude Sonnet: Save $42/week

"Find me savings"

User: Am I overpaying for my agents?
→ metrx_compare_models(models=["gpt-4o", "claude-3-5-sonnet", "gemini-1.5-pro"])

Model Comparison (per 1M tokens):
├── gpt-4o: $2.50 in / $10.00 out
├── claude-3-5-sonnet: $3.00 in / $15.00 out
└── gemini-1.5-pro: $3.50 in / $10.50 out

"Test a cheaper model"

User: Test Claude 3.5 Sonnet against my GPT-4 setup
→ metrx_create_model_experiment(agent_id="agent_123",
    model_a="gpt-4o", model_b="claude-3-5-sonnet-20241022", traffic_split=10)

Experiment started: 90% GPT-4o, 10% Claude 3.5 Sonnet
Check back in 14 days for statistical significance.

Companion Tool: Cost Leak Detector

This repo also includes @metrxbot/cost-leak-detector — a free, offline CLI that scans your LLM API logs for wasted spend. No signup, no cloud, no data leaves your machine.

npx @metrxbot/cost-leak-detector demo

It runs 7 checks (idle agents, premium model overuse, missing caching, high error rates, context overflow, no budgets, arbitrage opportunities) and gives you a scored report in seconds. See the full docs.

Configuration

API Key (required)

The server looks for your API key in this order:

METRX_API_KEY environment variable
~/.metrxrc file (created by --auth)

Run npx @metrxbot/mcp-server --auth to save your key, or set the env var directly.

Variable	Required	Description
`METRX_API_KEY`	Yes*	Your Metrx API key (get one free)
`METRX_API_URL`	No	Override API base URL (default: `https://metrxbot.com/api/v1`)

*Not required if you've run --auth — the key is read from ~/.metrxrc automatically.

CLI Flags

Flag	Description
`--demo`	Start with sample data — no API key or signup needed
`--auth`	Interactive login — opens browser, validates key, saves to `~/.metrxrc`
`--test`	Verify your API key and connection

Rate Limiting

60 requests per minute per tool. For higher limits, contact [email protected].

Development

git clone https://github.com/metrxbots/mcp-server.git
cd mcp-server
npm install
npm run typecheck
npm test

Contributing

See CONTRIBUTING.md for guidelines.

A Note on Naming

The product is Metrx (metrxbot.com). The npm scope is @metrxbot and the Smithery listing is metrxbot/mcp-server. The GitHub organization is metrxbots (with an s) because metrxbot was already taken on GitHub. If you see metrxbot vs metrxbots across platforms, they're the same project — just a GitHub namespace constraint.

License

MIT — see LICENSE.

💬 Feedback

Did Metrx work for you? We'd love to hear it — good or bad.

GitHub Discussions: Start a thread — questions, ideas, what you're building
Bug reports: Open an issue
Quick feedback: Drop a comment on our Product Hunt listing

If you installed but hit a snag, tell us what happened — we read every report.

MCP Server

README